局部地形对北京“7.21”特大暴雨影响的数值研究

利用NCEP1°×1°再分析资料和WRF模式对2012年7月21日北京特大暴雨过程进行数值模拟,结果表明,WRF模式能够成功模拟出本次暴雨过程,特别是对100 mm以上的大暴雨也有较强的模拟能力。基于控制试验,针对北京地区复杂的地形,设计了4组地形敏感性试验。试验结果表明:北京地区局部地形的改变对暴雨落区的模拟影响不大,但对暴雨中心的分布和强度有影响,特别是西南部的局部地形对于本次暴雨过程中降水中心的落区具有很大影响,而北部和东北部的局部地形则作用不明显。进一步研究表明,北京地区西南部的局部地形主要是通过影响低层偏南风急流的流场结构,改变强辐合区和垂直运动的分布和强度,进而引起降水中心位置和强度的改变。     

2012年8月中旬四川大暴雨的数值模拟及地形影响试验

利用WRF模式对2012年8月17~18日四川出现的一次大暴雨过程进行模式模拟和诊断分析,并通过对地形敏感性试验讨论川西高原至川东过渡带陡峭地形对此次暴雨的影响。结果表明,WRF基本成功模拟出2012年8月中旬四川中部大暴雨过程,对环流形式预报与实况较为一致。此次过程水汽来源台风低压及副高外围携带的水汽。川中云团发展阶段对应四川中部多个站出现强降水,该对流云团发展是引发本次降水的重要因素,而对流层中低层高温高湿环境对对流云团发展提供有力的环境。地形敏感性试验表明,陡峭地形高度与降水强度呈正比,通过影响陡峭地形周边物理量特征场分布从而影响暴雨区降水强度;相对湿度的影响小于温度对降水强度的影响作用。     

成都地区一次暴雨过程数值模拟及敏感性试验

本文利用探空资料和NCAR/NCEP全球再分析场资料, 对成都地区2009年8月25日的暴雨天气过程的环流背景和影响系统进行诊断分析。分析表明:此次暴雨过程为副高东撤南压后, 高原切变快速影响成都地区, 形成机制是副高外围的偏南气流结合河套地区南下的冷空气, 触发本地不稳定能量的爆发。利用WRF模式对此次暴雨过程进行数值模拟。模拟结果表明:WRF模式能够较好地模拟副热带高压的位置, 高原切变线及水汽来源, 模拟的雨带位置和雨量与实况较为一致, 模拟效果较好。通过修改下垫面性质, 对暴雨过程中下垫面的可能影响进行了敏感性数值试验。模拟结果表明:将新的下垫面资料引入后, 暴雨落区与控制试验相比变化较小, 暴雨区面积略有增加, 中雨范围扩大较为明显。分析原因为:成都地区周边的地形环境是影响成都中尺度降水总雨量和落区的关键影响因素。将盆地西部到东部的下垫面全部改为城镇类型后, 改变了辐射通量, 影响陆气之间的相互作用, 改变大气的能量平衡, 使之容易产生局地降水。      

成都地区一次暴雨过程数值模拟及敏感性试验

本文利用探空资料和NCAR/NCEP全球再分析场资料,对成都地区2009年8月25日的暴雨天气过程的环流背景和影响系统进行诊断分析。分析表明：此次暴雨过程为副高东撤南压后,高原切变快速影响成都地区,形成机制是副高外围的偏南气流结合河套地区南下的冷空气,触发本地不稳定能量的爆发。利用WRF模式对此次暴雨过程进行数值模拟。模拟结果表明：WRF模式能够较好地模拟副热带高压的位置,高原切变线及水汽来源,模拟的雨带位置和雨量与实况较为一致,模拟效果较好。通过修改下垫面性质,对暴雨过程中下垫面的可能影响进行了敏感性数值试验。模拟结果表明：将新的下垫面资料引入后,暴雨落区与控制试验相比变化较小,暴雨区面积略有增加,中雨范围扩大较为明显。分析原因为：成都地区周边的地形环境是影响成都中尺度降水总雨量和落区的关键影响因素。将盆地西部到东部的下垫面全部改为城镇类型后,改变了辐射通量,影响陆气之间的相互作用,改变大气的能量平衡,使之容易产生局地降水。     

耦合不同陆面过程对江西南部一次暖区特大暴雨过程的模拟试验

利用WRF V模式耦合4个陆面过程对2015年5月18—19日江西南部暖区特大暴雨进行了模拟,以检验陆面过程对暴雨过程的影响;在此基础上,又进行了地表通量敏感性试验,以检验地面扰动通量对降水的影响。结果表明,各方案模拟的降水范围、雨带走向以及降水中心位置与实况都比较相似,但是降水强度大小与陆面过程的选择存在一定的差异。其中,WRF模式耦合NOAH方案模拟的降水中心、降水强度和累积降水量均能够较好地反映这次暴雨过程的发展。感热和潜热通量在暖区暴雨中是维持降水持续的主要能量输送,对暴雨中心强度和位置变化的影响,潜热通量比感热通量发挥了更重要的作用。来自地面水汽蒸发所释放的潜热以及水汽抬升和辐合释放的潜能为维持暴雨强度提供了重要的能量支撑。     

华南暖区暴雨预报的模式初始场质量敏感性分析

本文利用高分辨率中尺度数值预报模式WRF和两组再分析资料,在研究不同模式初值对华南暖区暴雨预报质量差异明显的基础上,利用合成初值方法进行了模式初值对暖区暴雨预报的敏感性数值试验研究,讨论了模式初始场关键物理量对暖区暴雨预报质量的影响,重点开展了模式初值湿度场质量对华南暖区暴雨降水预报的敏感性分析。结果表明：模式初始场质量的较小差异,可显著影响本次华南暖区暴雨预报的降水强度、降水落区以及降水发生时间等的质量。初始水汽场对暖区暴雨预报影响最大,也最为敏感,是准确预报对流单体的发生发展以及地面强降水的基础。风场和温度场对暖区暴雨预报的影响相对较小。对流层低层较强的风速辐合是本次暖区暴雨强对流单体触发、生成和加强发展以至产生暖区强降水的物理基础。     

一次典型东北冷涡暴雨的数值模拟及诊断分析

利用中尺度数值模式WRF,模拟了黑龙江省2004年8月28～29日出现的暴雨过程,并对此次过程进行诊断分析。结果表明。WRF中尺度模式能够较好的模拟冷涡暴雨天气,并从暴雨形成的辐合辐散、水汽、垂直速度等物理量诊断分析此次暴雨过程的演变及物理特征。     

不同边界层和陆面过程参数化方案对比分析

利用WRF模式选用不同的边界层参数化方案 (YSU、MRF) 结合三种陆面过程方案 (RUC、SLAB、Noah) , 模拟了2011年5月1~3日的四川东部暴雨过程, 对不同参数化方案结合不同陆面过程结果进行对比试验基础上发现, 模式对24h降水落区及强度有较强预报能力, 但对单站小时降水分布的预报能力还需改进;不同边界层方案与陆面过程的对比试验说明降水对于边界层物理过程有一定敏感性, 各试验的差异主要体现在对暴雨中心雨强以及降水峰值强度和峰值出现时段的预报上;WRF模式基本上能够模拟出边界层要素日变化特征。     

一次特大暴雨的集合预报试验

利用NCEP 1°×1°再分析资料,采用WRFV2.2模式对2008年6月12-13日的一次广西特大暴雨天气过程进行了包括积云对流参数化方案、边界层参数化方案、陆面过程参数化方案的集合预报试验.结果表明:模拟降水对WRF模式不同的物理过程表现出不同的敏感性,对积云对流参数化方案最敏感,降水离散度最大;对陆面过程参数化方...     

淮河流域暴雨过程的数值模拟和诊断分析

利用中尺度数值模式WRF输出结果，对2003年7月4-6日淮河流域出现的暴雨过程进行了诊断分析。结果表明，WRF模式对这次暴雨过程的雨带位置和走向均有较好的模拟，但暴雨中心位置偏移。这次暴雨过程中尺度天气系统的发生、发展、加强及衰减均在模拟结果中有所显现。     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

<正>The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth’s climate and environment.     

VARIABILITY OF INTER-ANNUAL RELATIONSHIP BETWEEN INDIAN OCEAN DIPOLE AND HUAXI REGION’S AUTUMN PRECIPITATION

The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region.     

A COMPARATIVE ANALYSIS OF ATMOSPHERIC AND OCEANIC CONDITIONS BEFORE THE OCCURRENCE OF TWO TYPES OF EL NIO EVENTS

The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o.     

IMPACT OF ATMOSPHERIC LOW-FREQUENCY OSCILLATION ON THE IMMIGRATION OF NILAPARVATA LUGENS(STL) IN HUNAN AND JIANGXI PROVINCES

Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.     

全球贸易隐含碳变化及其影响分析

基于最新的GTAP8 (Global Trade Analysis Project）数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;